Digital control systems can be used to control various physical operations. One application for such digital control systems is the automotive internal combustion engine of a vehicle. In particular, one feature of automotive digital control systems relates to adaptively learning system errors, such as vehicle to vehicle variations in fuel injector characteristics, pedal position sensor variations, variations in process parameters over time, and various other applications.
In many cases, the ability to adaptively learn information is constrained due to limited amounts of data. For example, there is often a competition for certain operating conditions where adaptive learning is utilized. This results in a need to develop methods for using the limited amount of data to adapt and learn as much information as possible about the system.
Once such method used in such cases involves reverse interpolation. Such a method is described in U.S. Pat. No. 6,542,790.
The inventors herein, however, have recognized that there are other situations where adaptive learning can be applied where there is more than enough information from which parameters can be adaptively learned. The inventors herein have further recognized that, in cases where there is surplus information, the approaches of the prior art become a chronometric drain, and can result in inaccurate learning, unlearning, and relearning of information.
The above disadvantage can be overcome by a computer storage medium having instructions encoded therein for controlling an engine of a powertrain in a vehicle on the road. The medium comprises code for measuring an error for a first operating condition based on sensor information; code for determining whether said first operating condition is within a predetermined range of a second operating condition; and code for updating an adaptively learned parameter for said second operating condition based on said error when said first operating condition is within said predetermined range of said second operating condition.
In one example, the medium further comprises code for discarding said error when said first operating condition is outside said predetermined range of said second operating condition.
As such, in systems where there is sufficient surplus data, information can be learned when the current operating conditions are near the conditions at which learned data is saved; while at the same time, surplus data can be discarded when the current operating conditions are outside the conditions at which learned data is saved. In this way, more accurate data learning is possible without the disadvantages associated with reverse interpolation.
In another example, the above disadvantages can be overcome by a computer storage medium having instructions encoded therein for controlling an engine of a powertrain in a vehicle on the road. The medium comprises code for measuring an error for a first set of vehicle operating conditions based on sensor information; code for determining whether said first set of vehicle operating conditions is within a predetermined range of a second set of vehicle operating conditions saved in memory of said computer; and code for updating an adaptively learned parameter saved in said computer memory, said adaptively learned parameter corresponding to said second set of vehicle operating conditions, said updating said adaptively learned parameter based on said error when said first set of vehicle operating conditions is within said predetermined range of said second set of vehicle operating conditions.
In this way, it is possible to provide increase accuracy in adaptive learning.
An example advantage of the above aspects is reduced computation needs and convergence learning time.